In the general artificial intelligence field, systems operating on the basis of said artificial intelligence must be enabled to acquire information from the surrounding world and use this information in order to enhance information and metadata that are not immediately visible or to highlight features or structures or events that are not readily visible.
An usual common practice is to operate through a fusion of data from different multi-parametric datasets, that is, from a certain number of datasets that include data related, at least partially, to different parameters, or from the dataset that alternatively or in combination with the fact of having data for at least a part of different parameters presents data collected in different time instants.
The data fusion technique is widely known and described for example in the document Dasarthy, Decision Fusion, IEEE Computer Society Press, 1994.
The fusion is advantageous for different purposes such as detection, recognition, identification, tracking, decision making. These goals are pursued in a large number of different application fields such as robotics, medicine, geological monitoring and many other fields.
A major fusion aim is the improvement of the reliability related to decision making processes executed by automated or robotic machines or operating with artificial intelligence.
For example, thanks to additional or complementary information through the acquisition of images in different modalities, with different sensors, and the fusion of these images, the information about the object depicted in the image can be improved by the fusion of image data for these objects, and then the reliability of the decision choice is improved dependent on the image information content, both at the level of human decision or performed by an artificial-based machine intelligence.
The data fusion systems combine multiple sources of original data to each other to make new sets of data, in which information is organized differently and whose content can better be extracted from the data.
Actually, the known data-fusion systems are not satisfactory with respect to the objective of fusing elements of information one with each other in a way targeted to the detection of the data structures of interest.
Systems are known for processing data that operate on the data so as to operate at the level of features (characteristics) represented by the data itself. The information space of a set of data may be subjected to a transformation which generates a new basis of orthogonal vectors that describe the so-called features where it is possible to represent the data of said set. A known method that operates this transformation is the so-called Principal Component Analysis (PCA).
A more detailed and rigorous description of this technique is reported in the document Principal Component Analysis IT Jolliffe, Second edition, Springer series in statistics, ISBNO-387-95442-2, 2002. This description is considered part of the present description.
This transform describes the data as a combination of several “features”, generating a vector of “features” that essentially identifies the information represented by the data organizing it from the most relevant information to the less important ones.